Multiple　phases　with　transitions　from　phase　to　phase　are　important　characteristics　of　many　batch　processes.　To　overcome　the　hard-partition　and　misclassification　problems,　and　also　to　monitor　batch　processes　more　accurately　and　efficiently,　such　process　features　are　needed　to　be　considered　carefully.　In　this　paper,　a　novel　multiple　PCA　batch　monitoring　and　fault　diagnosing　approach　based　on　fuzzy　clustering　soft-partition　is　proposed.　The　proposed　method　calculates　firstly　the　similarity　indices　between　different　time-slice　data　matrices　of　batch　processes.　Then　phase　division　algorithm　is　designed　with　fuzzy　clustering　based　on　similarity　index,　and　a　fuzzy　membership　grade　transition　identification　step　is　following.　By　setting　a　series　of　multiple　PCA　models　with　time-varying　covariance　structures,　the　method　reflects　objectively　the　diversity　of　transitional　characteristics　and　can　preferably　solve　the　stage-transition　monitoring　problem　in　multistage　batch　processes.　The　proposed　method　was　used　to　evaluate　the　industrial　penicillin　fermentation　process　data.　The　results　clearly　demonstrate　the　power　and　advantages　of　the　proposed　method　in　comparison　with　conventional　MPCA　and　sub-PCA　method.